Update app.py

app.py

@@ -1,5 +1,3 @@
-import os
-
 import gradio as gr
 from datasets import Audio
 from datasets import load_dataset
@@ -9,7 +7,8 @@ from transformers import pipeline
 from arabic_normalizer import ArabicTextNormalizer
 
 # Load dataset
-common_voice = load_dataset("mozilla-foundation/common_voice_11_0",trust_remote_code=True, name = "ar", split = "train")
+common_voice = load_dataset("mozilla-foundation/common_voice_11_0", trust_remote_code = True, name = "ar",
+                            split = "train")
 # select column that will be used
 common_voice = common_voice.select_columns(["audio", "sentence"])
 
@@ -21,7 +20,10 @@ generate_kwargs = {
 asr_whisper_large = pipeline("automatic-speech-recognition", model = "openai/whisper-large-v3",
                              generate_kwargs = generate_kwargs)
 asr_whisper_large_turbo = pipeline("automatic-speech-recognition", model = "openai/whisper-large-v3-turbo",
-                                    generate_kwargs = generate_kwargs)
+                                   generate_kwargs = generate_kwargs)
+asr_whisper_large_turbo_mboushaba = pipeline("automatic-speech-recognition", model =
+"mboushaba/whisper-large-v3-turbo-arabic",
+                                             generate_kwargs = generate_kwargs)
 normalizer = ArabicTextNormalizer()
 
 
@@ -54,14 +56,20 @@ def generate_audio(index = None):
         "sampling_rate": audio["sampling_rate"]
     }
 
+    audio_data_turbo_mboushaba = {
+        "raw": audio["array"],
+        "sampling_rate": audio["sampling_rate"]
+    }
+
     # Perform automatic speech recognition (ASR) directly on the resampled audio array
     asr_output = asr_whisper_large(audio_data)
-
     asr_output_turbo = asr_whisper_large_turbo(audio_data_turbo)
+    asr_output_turbo_mboushaba = asr_whisper_large_turbo_mboushaba(audio_data_turbo_mboushaba)
 
     # Extract the transcription from the ASR model output
     predicted_text = normalizer(asr_output["text"])
     predicted_text_turbo = normalizer(asr_output_turbo["text"])
+    predicted_text_turbo_mboushaba = normalizer(asr_output_turbo_mboushaba["text"])
 
     # Compute WER, Word Accuracy, and CER
     wer_score = wer(reference_text, predicted_text)
@@ -70,52 +78,74 @@ def generate_audio(index = None):
     wer_score_turbo = wer(reference_text, predicted_text_turbo)
     cer_score_turbo = cer(reference_text, predicted_text_turbo)
 
+    wer_score_turbo_mboushaba = wer(reference_text, predicted_text_turbo_mboushaba)
+    cer_score_turbo_mboushaba = cer(reference_text, predicted_text_turbo_mboushaba)
+
     # Prepare display data: original sentence, sampling rate, ASR transcription, and metrics
     sentence_info = "-".join([reference_text, str(audio["sampling_rate"])])
 
-    return ((
-                audio["sampling_rate"],
-                audio["array"]
-            ), sentence_info, predicted_text, wer_score, cer_score, predicted_text_turbo,
-            wer_score_turbo, cer_score_turbo)
+    return {
+        "audio": (
+            audio["sampling_rate"],
+            audio["array"]
+        ),
+        "sentence_info": sentence_info,
+        "predicted_text": predicted_text,
+        "wer_score": wer_score,
+        "cer_score": cer_score,
+        "predicted_text_turbo": predicted_text_turbo,
+        "wer_score_turbo": wer_score_turbo,
+        "cer_score_turbo": cer_score_turbo,
+        "predicted_text_turbo_mboushaba": predicted_text_turbo_mboushaba,
+        "wer_score_turbo_mboushaba": wer_score_turbo_mboushaba,
+        "cer_score_turbo_mboushaba": cer_score_turbo_mboushaba
+    }
+
 
 def update_ui():
     res = []
     for i in range(4):
-        res.append(gr.Textbox(label=f"Label {i}"))
+        res.append(gr.Textbox(label = f"Label {i}"))
     return res
 
-with (gr.Blocks() as demo):
+
+with gr.Blocks() as demo:
     gr.HTML("""
         <h1>Whisper Arabic: ASR Comparison (large and large turbo)</h1>""")
     gr.Markdown("""
         This is a demo to compare the outputs, WER & CER of two ASR models (Whisper large and large turbo) using 
         arabic dataset from mozilla-foundation/common_voice_11_0
     """)
-    num_samples_input = gr.Slider(minimum=1, maximum=10, step=1, value=4, label="Number of audio samples")
+    num_samples_input = gr.Slider(minimum = 1, maximum = 10, step = 1, value = 4, label = "Number of audio samples")
     generate_button = gr.Button("Generate Samples")
 
 
-    @gr.render(inputs=num_samples_input, triggers=[generate_button.click])
+    @gr.render(inputs = num_samples_input, triggers = [generate_button.click])
     def render(num_samples):
         with gr.Column():
             for i in range(num_samples):
                 # Generate audio and associated data
-                _audio, label, asr_text, wer_score, cer_score, asr_text_turbo, wer_score_turbo, cer_score_turbo =generate_audio()
+                data = generate_audio()
 
                 # Create Gradio components to display the audio, transcription, and metrics
-                gr.Audio(_audio, label = label)
+                gr.Audio(data["audio"], label = data["sentence_info"])
                 with gr.Row():
                     with gr.Column():
-                        gr.Textbox(value = asr_text, label = "Whisper large output"),
-                        gr.Textbox(value = f"WER: {wer_score:.2f}", label = "Word Error Rate"),
-                        gr.Textbox(value = f"CER: {cer_score:.2f}", label = "Character Error Rate"),
+                        gr.Textbox(value = data["predicted_text"], label = "Whisper large output"),
+                        gr.Textbox(value = f"WER: {data['wer_score']:.2f}", label = "Word Error Rate"),
+                        gr.Textbox(value = f"CER: {data['cer_score']:.2f}", label = "Character Error Rate"),
+                    with gr.Column():
+                        gr.Textbox(value = data["predicted_text_turbo"], label = "Whisper large turbo output"),
+                        gr.Textbox(value = f"WER: {data['wer_score_turbo']:.2f}", label = "Word Error Rate - "
+                                                                                  "TURBO  "),
+                        gr.Textbox(value = f"CER: {data['cer_score_turbo']:.2f}", label = "Character Error "
+                                                                                  "Rate - TURBO")
                     with gr.Column():
-                        gr.Textbox(value = asr_text_turbo, label = "Whisper large turbo output"),
-                        gr.Textbox(value = f"WER: {wer_score_turbo:.2f}", label = "Word Error Rate - "
-                                                                                                   "TURBO  "),
-                        gr.Textbox(value = f"CER: {cer_score_turbo:.2f}", label = "Character Error "
-                                                                                                      "Rate - TURBO")
-
-if __name__ == '__main__':
-    demo.launch(show_error = True)
+                        gr.Textbox(value = data["predicted_text_turbo_mboushaba"], label = "Whisper large turbo "
+                                                                                           "mboushaba output"),
+                        gr.Textbox(value = f"WER: {data['wer_score_turbo_mboushaba']:.2f}", label = "Word Error Rate - "
+                                                                                  " mboushaba TURBO  "),
+                        gr.Textbox(value = f"CER: {data['cer_score_turbo_mboushaba']:.2f}", label = "Character Error "
+                                                                                  "Rate - mboushaba TURBO")
+
+demo.launch(show_error = True)